


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							old_name

     if "patch_embed" in old_name:
          snake_case					,    snake_case					,    snake_case											=							old_name.split('''.'''							)

          if layer == "0":
               snake_case											=							old_name.replace('''0'''				,'''convolution1'''							)
          elif layer == "1":
               snake_case											=							old_name.replace('''1'''				,'''batchnorm_before'''							)
          elif layer == "3":
               snake_case											=							old_name.replace('''3'''				,'''convolution2'''							)
          else:
               snake_case											=							old_name.replace('''4'''				,'''batchnorm_after'''							)

     if "network" in old_name and re.search(r'''\d\.\d'''				,UpperCamelCase_							):
          snake_case											=							r'''\b\d{2}\b'''
          if bool(re.search(UpperCamelCase_				,UpperCamelCase_							)							):
               snake_case											=							re.search(r'''\d\.\d\d.'''				,UpperCamelCase_							).group()
          else:
               snake_case											=							re.search(r'''\d\.\d.'''				,UpperCamelCase_							).group()
          if int(match[0]							) < 6:
               snake_case											=							old_name.replace(UpperCamelCase_				,''''''							)
               snake_case											=							trimmed_name.replace('''network'''				,match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]							)
               snake_case											=							'''intermediate_stages.''' + trimmed_name
          else:
               snake_case											=							old_name.replace(UpperCamelCase_				,''''''							)
               if int(match[2]							) < num_meta4D_last_stage:
                    snake_case											=							trimmed_name.replace('''network'''				,'''meta4D_layers.blocks.''' + match[2]							)
               else:
                    snake_case											=							str(int(match[2]							) - num_meta4D_last_stage							)
                    snake_case											=							trimmed_name.replace('''network'''				,'''meta3D_layers.blocks.''' + layer_index							)
                    if "norm1" in old_name:
                         snake_case											=							trimmed_name.replace('''norm1'''				,'''layernorm1'''							)
                    elif "norm2" in old_name:
                         snake_case											=							trimmed_name.replace('''norm2'''				,'''layernorm2'''							)
                    elif "fc1" in old_name:
                         snake_case											=							trimmed_name.replace('''fc1'''				,'''linear_in'''							)
                    elif "fc2" in old_name:
                         snake_case											=							trimmed_name.replace('''fc2'''				,'''linear_out'''							)

               snake_case											=							'''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(r'''.\d.'''				,UpperCamelCase_							):
          snake_case											=							old_name.replace('''network'''				,'''intermediate_stages'''							)

     if "fc" in new_name:
          snake_case											=							new_name.replace('''fc'''				,'''convolution'''							)
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          snake_case											=							new_name.replace('''norm1'''				,'''batchnorm_before'''							)
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          snake_case											=							new_name.replace('''norm2'''				,'''batchnorm_after'''							)
     if "proj" in new_name:
          snake_case											=							new_name.replace('''proj'''				,'''projection'''							)
     if "dist_head" in new_name:
          snake_case											=							new_name.replace('''dist_head'''				,'''distillation_classifier'''							)
     elif "head" in new_name:
          snake_case											=							new_name.replace('''head'''				,'''classifier'''							)
     elif "patch_embed" in new_name:
          snake_case											=							'''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          snake_case											=							new_name.replace('''norm'''				,'''layernorm'''							)
          snake_case											=							'''efficientformer.''' + new_name
     else:
          snake_case											=							'''efficientformer.encoder.''' + new_name

     return new_name
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     for key in checkpoint.copy().keys():
          snake_case											=							checkpoint.pop(UpperCamelCase_							)
          snake_case											=							val

     return checkpoint
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     snake_case											=							Image.open(requests.get(UpperCamelCase_				,stream=UpperCamelCase_							).raw							)

     return image
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							torch.load(UpperCamelCase_				,map_location='''cpu'''							)['''model''']
     snake_case											=							EfficientFormerConfig.from_json_file(UpperCamelCase_							)
     snake_case											=							EfficientFormerForImageClassificationWithTeacher(UpperCamelCase_							)
     snake_case											=							'''_'''.join(checkpoint_path.split('''/'''							)[-1].split('''.'''							)[0].split('''_'''							)[:-1]							)

     snake_case											=							config.depths[-1] - config.num_metaad_blocks + 1
     snake_case											=							convert_torch_checkpoint(UpperCamelCase_				,UpperCamelCase_							)

     model.load_state_dict(UpperCamelCase_							)
     model.eval()

     snake_case											=							{
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     snake_case											=							prepare_img()
     snake_case											=							2_56
     snake_case											=							2_24
     snake_case											=							EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size}				,crop_size={'''height''': crop_size, '''width''': crop_size}				,resample=pillow_resamplings['''bicubic''']				,)
     snake_case											=							processor(images=UpperCamelCase_				,return_tensors='''pt'''							).pixel_values

     # original processing pipeline
     snake_case											=							Compose(
         [
             Resize(UpperCamelCase_				,interpolation=pillow_resamplings['''bicubic''']							),
             CenterCrop(UpperCamelCase_							),
             ToTensor(),
             Normalize(UpperCamelCase_				,UpperCamelCase_							),
         ]							)
     snake_case											=							image_transforms(UpperCamelCase_							).unsqueeze(0							)

     assert torch.allclose(UpperCamelCase_				,UpperCamelCase_							)

     snake_case											=							model(UpperCamelCase_							)
     snake_case											=							outputs.logits

     snake_case											=							(1, 10_00)

     if "l1" in model_name:
          snake_case											=							torch.Tensor(
              [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]							)
          assert torch.allclose(logits[0, :10]				,UpperCamelCase_				,atol=1e-3							)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          snake_case											=							torch.Tensor(
              [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]							)
          assert torch.allclose(logits[0, :10]				,UpperCamelCase_				,atol=1e-3							)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          snake_case											=							torch.Tensor(
              [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]							)
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''							)

     # Save Checkpoints
     Path(UpperCamelCase_							).mkdir(exist_ok=UpperCamelCase_							)
     model.save_pretrained(UpperCamelCase_							)
     print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)
     processor.save_pretrained(UpperCamelCase_							)
     print(F'''Processor successfuly saved at {pytorch_dump_path}'''							)

     if push_to_hub:
          print('''Pushing model to the hub...'''							)

          model.push_to_hub(
              repo_id=F'''Bearnardd/{pytorch_dump_path}'''				,commit_message='''Add model'''				,use_temp_dir=UpperCamelCase_				,)
          processor.push_to_hub(
              repo_id=F'''Bearnardd/{pytorch_dump_path}'''				,commit_message='''Add image processor'''				,use_temp_dir=UpperCamelCase_				,)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path",
     default=None,
     type=str,
     required=True,
     help="Path to EfficientFormer pytorch checkpoint.",
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for EfficientFormer model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )

 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )
 parser.set_defaults(push_to_hub=True)

 _SCREAMING_SNAKE_CASE     :					Any      =       parser.parse_args()
 convert_efficientformer_checkpoint(
     checkpoint_path=args.pytorch_model_path,
     efficientformer_config_file=args.config_file,
     pytorch_dump_path=args.pytorch_dump_path,
     push_to_hub=args.push_to_hub,
 )








import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Any      =       logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE     :					str      =       {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_SCREAMING_SNAKE_CASE     :					Any      =       {
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

_SCREAMING_SNAKE_CASE     :					List[Any]      =       {
    "allenai/longformer-base-4096": 40_96,
    "allenai/longformer-large-4096": 40_96,
    "allenai/longformer-large-4096-finetuned-triviaqa": 40_96,
    "allenai/longformer-base-4096-extra.pos.embd.only": 40_96,
    "allenai/longformer-large-4096-extra.pos.embd.only": 40_96,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							(
         list(range(ord('''!'''							)				,ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,ord('''ÿ'''							) + 1							)							)
     )
     snake_case											=							bs[:]
     snake_case											=							0
     for b in range(2**8							):
          if b not in bs:
               bs.append(UpperCamelCase_							)
               cs.append(2**8 + n							)
               n += 1
     snake_case											=							[chr(UpperCamelCase_							) for n in cs]
     return dict(zip(UpperCamelCase_				,UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							set()
     snake_case											=							word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)							)
          snake_case											=							char
     return pairs

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						VOCAB_FILES_NAMES
    __magic_name__         =						PRETRAINED_VOCAB_FILES_MAP
    __magic_name__         =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__         =						['input_ids', 'attention_mask']
    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case="replace"     ,   __snake_case="<s>"     ,   __snake_case="</s>"     ,   __snake_case="</s>"     ,   __snake_case="<s>"     ,   __snake_case="<unk>"     ,   __snake_case="<pad>"     ,   __snake_case="<mask>"     ,   __snake_case=False     ,   **__snake_case     ,   ):
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else bos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else eos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else sep_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else cls_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else unk_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else mask_token

         super().__init__(
             errors=__snake_case     ,   bos_token=__snake_case     ,   eos_token=__snake_case     ,   unk_token=__snake_case     ,   sep_token=__snake_case     ,   cls_token=__snake_case     ,   pad_token=__snake_case     ,   mask_token=__snake_case     ,   add_prefix_space=__snake_case     ,   **__snake_case     ,   )

         with open(__snake_case     ,   encoding='''utf-8'''       ) as vocab_handle:
              snake_case											=							json.load(__snake_case       )
         snake_case											=							{v: k for k, v in self.encoder.items()}
         snake_case											=							errors  # how to handle errors in decoding
         snake_case											=							bytes_to_unicode()
         snake_case											=							{v: k for k, v in self.byte_encoder.items()}
         with open(__snake_case     ,   encoding='''utf-8'''       ) as merges_handle:
              snake_case											=							merges_handle.read().split('''\n'''       )[1:-1]
         snake_case											=							[tuple(merge.split()       ) for merge in bpe_merges]
         snake_case											=							dict(zip(__snake_case     ,   range(len(__snake_case       )       )       )       )
         snake_case											=							{}
         snake_case											=							add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         snake_case											=							re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )
    @property
    def 						a_     (			self       ):
         return len(self.encoder       )
    def 						a_     (			self       ):
         return dict(self.encoder     ,   **self.added_tokens_encoder       )
    def 						a_     (			self     ,   __snake_case       ):
         if token in self.cache:
              return self.cache[token]
         snake_case											=							tuple(__snake_case       )
         snake_case											=							get_pairs(__snake_case       )

         if not pairs:
              return token

         while True:
              snake_case											=							min(__snake_case     ,   key=lambda __snake_case       : self.bpe_ranks.get(__snake_case     ,   float('''inf'''       )       )       )
              if bigram not in self.bpe_ranks:
                   break
              snake_case					,    snake_case											=							bigram
              snake_case											=							[]
              snake_case											=							0
              while i < len(__snake_case       ):
                   try:
                        snake_case											=							word.index(__snake_case     ,   __snake_case       )
                   except ValueError:
                        new_word.extend(word[i:]       )
                        break
                   else:
                        new_word.extend(word[i:j]       )
                        snake_case											=							j

                   if word[i] == first and i < len(__snake_case       ) - 1 and word[i + 1] == second:
                        new_word.append(first + second       )
                        i += 2
                   else:
                        new_word.append(word[i]       )
                        i += 1
              snake_case											=							tuple(__snake_case       )
              snake_case											=							new_word
              if len(__snake_case       ) == 1:
                   break
              else:
                   snake_case											=							get_pairs(__snake_case       )
         snake_case											=							''' '''.join(__snake_case       )
         snake_case											=							word
         return word
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							[]
         for token in re.findall(self.pat     ,   __snake_case       ):
              snake_case											=							''''''.join(
                  self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case       ).split(''' '''       )       )
         return bpe_tokens
    def 						a_     (			self     ,   __snake_case       ):
         return self.encoder.get(__snake_case     ,   self.encoder.get(self.unk_token       )       )
    def 						a_     (			self     ,   __snake_case       ):
         return self.decoder.get(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							''''''.join(__snake_case       )
         snake_case											=							bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     ,   errors=self.errors       )
         return text
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if not os.path.isdir(__snake_case       ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
              return
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as f:
              f.write(json.dumps(self.encoder     ,   indent=2     ,   sort_keys=__snake_case     ,   ensure_ascii=__snake_case       ) + '''\n'''       )

         snake_case											=							0
         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as writer:
              writer.write('''#version: 0.2\n'''       )
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,   key=lambda __snake_case       : kv[1]       ):
                   if index != token_index:
                        logger.warning(
                            F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                            ''' Please check that the tokenizer is not corrupted!'''       )
                        snake_case											=							token_index
                   writer.write(''' '''.join(__snake_case       ) + '''\n'''       )
                   index += 1

         return vocab_file, merge_file
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         snake_case											=							[self.cls_token_id]
         snake_case											=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = False       ):
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__snake_case     ,   token_ids_a=__snake_case     ,   already_has_special_tokens=__snake_case       )

         if token_ids_a is None:
              return [1] + ([0] * len(__snake_case       )) + [1]
         return [1] + ([0] * len(__snake_case       )) + [1, 1] + ([0] * len(__snake_case       )) + [1]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							[self.sep_token_id]
         snake_case											=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False     ,   **__snake_case       ):
         snake_case											=							kwargs.pop('''add_prefix_space'''     ,   self.add_prefix_space       )
         if (is_split_into_words or add_prefix_space) and (len(__snake_case       ) > 0 and not text[0].isspace()):
              snake_case											=							''' ''' + text
         return (text, kwargs)




